{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Science Tech Brief By HackerNoon","title":"Navigating the Maze of Multiple Hypotheses Testing—Part 2: Practical Implementation ","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/94436fe7\"></iframe>","width":"100%","height":180,"duration":221,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/navigating-the-maze-of-multiple-hypotheses-testingpart-2-practical-implementation.\nIn this article, we will explore practical implementation with Python code and interpretation of the results.\nCheck more stories related to science at: https://hackernoon.com/c/science.\n            You can also check exclusive content about #statistics, #python, #data-analysis, #bonferroni-correction, #hypothesis-testing, #statistical-significance, #p-values, #data-interpretation,  and more.\nThis story was written by: @vabars. Learn more about this writer by checking @vabars's about page,\n            and for more stories, please visit hackernoon.com.\nIn this article, we will explore practical implementation with Python code and interpretation of the results. The Bonferroni correction makes the p-values higher to control for the increased risk of Type I errors (false positives) that come with multiple testing. In this case, the first (`True`) and last (` true`) hypotheses are rejected.","thumbnail_url":"https://img.transistorcdn.com/S66fL9skYMhlajDauLWqBH_bXds_u8JsPbvAZlh45OA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjczLzE2ODM1/ODI0MjQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}